pull out tuple utils

src/backend/mxnet/model.jl

@@ -1,3 +1,5 @@
+using Flux: collectt, shapecheckt
+
 struct AlterParam
   param
   load
@@ -46,22 +48,10 @@ mxgroup(x::Tuple) = mx.Group(mxgroup.(x)...)
 mxungroup(x, outs) = copy(shift!(outs))
 mxungroup(x::Tuple, outs) = map(x -> mxungroup(x, outs), x)
 
-function collectt(xs)
-  ys = []
-  mapt(x -> push!(ys, x), xs)
-  return ys
-end
-
-function dictt(ks::Tuple, vs, d = Dict())
-  for i = 1:length(ks)
-    dictt(ks[i], vs[i], d)
-  end
-  return d
-end
-
-dictt(k, v, d = Dict()) = (d[k] = v; d)
+dictt(xs, ys) = Dict(zip(collectt(xs), collectt(ys)))
 
 function executor(graph::Graph, input...)
+  shapecheckt(graph.input, input)
   args  = merge(mxparams(graph), dictt(graph.input, mapt(d->MXArray(size(d)), input)))
   grads = merge(mxparams(graph), dictt(graph.input, mapt(d->MXArray(size(d)), input)))
   exec = mx.bind(mxgroup(graph.output),

src/utils.jl

@@ -1,5 +1,7 @@
 export AArray, unsqueeze
 
+# Arrays
+
 const AArray = AbstractArray
 
 initn(dims...) = randn(dims...)/100
@@ -10,11 +12,29 @@ Base.squeeze(xs) = squeeze(xs, 1)
 stack(xs, dim = 1) = cat(dim, unsqueeze.(xs, dim)...)
 unstack(xs, dim = 1) = [slicedim(xs, dim, i) for i = 1:size(xs, dim)]
 
+convertel(T::Type, xs::AbstractArray) = convert.(T, xs)
+convertel{T}(::Type{T}, xs::AbstractArray{T}) = xs
+
+# Tuples
+
 mapt(f, x) = f(x)
 mapt(f, xs::Tuple) = map(x -> mapt(f, x), xs)
 
-convertel(T::Type, xs::AbstractArray) = convert.(T, xs)
-convertel{T}(::Type{T}, xs::AbstractArray{T}) = xs
+function collectt(xs)
+  ys = []
+  mapt(x -> push!(ys, x), xs)
+  return ys
+end
+
+function shapecheckt(xs::Tuple, ys::Tuple)
+  length(xs) == length(ys) || error("Expected tuple length $(length(xs)), got $ys")
+  shapecheckt.(xs, ys)
+end
+
+shapecheckt(xs::Tuple, ys) = error("Expected tuple, got $ys")
+shapecheckt(xs, ys) = nothing
+
+# Other
 
 function accuracy(m, data)
   n = 0